class_docstr: Speech enhancement or separation Frontend model
code: def forward_loss(
      self,
      speech_pre: torch.Tensor,
      speech_lengths: torch.Tensor,
      feature_mix: torch.Tensor,
      feature_pre: List[torch.Tensor],
      others: OrderedDict,
      speech_ref: List[torch.Tensor],
      noise_ref: Optional[List[torch.Tensor]] = None,
      dereverb_speech_ref: Optional[List[torch.Tensor]] = None,
      category: Optional[torch.Tensor] = None,
      num_spk: Optional[int] = None,
      fs: Optional[int] = None,
  ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
      # for calculating loss on estimated noise signals
      if getattr(self.separator, "predict_noise", False):
          assert "noise1" in others, others.keys()
      if noise_ref is not None and "noise1" in others:
          for n in range(self.num_noise_type):
              key = "noise{}".format(n + 1)
              others[key] = self.decoder(
                  others[key], speech_lengths, fs=fs
              )[0]
      # for calculating loss on dereverberated signals
      if getattr(self.separator, "predict_dereverb", False):
          assert "dereverb1" in others, others.keys()
      if dereverb_speech_ref is not None and "dereverb1" in others:
          for spk in range(num_spk if num_spk else self.num_spk):
              key = "dereverb{}".format(spk + 1)
              if key in others:
                  others[key] = self.decoder(
                      others[key], speech_lengths, fs=fs
                  )[0]
  
      loss = speech_ref[0].new_tensor(0.0)
      stats = {}
      o = {}
      perm = None
      for loss_wrapper in self.loss_wrappers:
          criterion = loss_wrapper.criterion
          only_for_test = getattr(criterion, "only_for_test", False)
          if only_for_test and self.training:
              continue
          is_noise_loss = getattr(criterion, "is_noise_loss", False)
          is_dereverb_loss = getattr(
              criterion, "is_dereverb_loss", False
          )
          if is_noise_loss:
              if noise_ref is None:
                  raise ValueError(
                      "No noise reference for training!\n"
                      'Please specify "--use_noise_ref true" in run.sh'
                  )
              signal_ref = noise_ref
              signal_pre = [
                  others["noise{}".format(n + 1)]
                  for n in range(self.num_noise_type)
              ]
          elif is_dereverb_loss:
              if dereverb_speech_ref is None:
                  raise ValueError(
                      "No dereverberated reference for training!\n"
                      'Please specify "--use_dereverb_ref true" in run.sh'
                  )
              signal_ref = dereverb_speech_ref
              signal_pre = [
                  others["dereverb{}".format(n + 1)]
                  for n in range(self.num_noise_type)
                  if "dereverb{}".format(n + 1) in others
              ]
              if len(signal_pre) == 0:
                  signal_pre = None
          else:
              signal_ref = speech_ref
              signal_pre = speech_pre
  
          zero_weight = loss_wrapper.weight == 0.0
          if isinstance(criterion, TimeDomainLoss):
              if signal_pre is None:
                  if is_noise_loss or is_dereverb_loss:
                      # Skip loss computation for noise/dereverb-specific losses
                      # if no noise/dereverb signals are predicted for this batch
                      if category is not None:
                          for idx, c in self.categories.items():
                              stats[
                                  criterion.name + "_" + c
                              ] = torch.full_like(loss, np.nan)
                      else:
                          stats[criterion.name] = torch.full_like(
                              loss, np.nan
                          )
                      continue
                  raise ValueError(
                      "Predicted waveform is required for time-domain loss."
                  )
              sref, spre = self._align_ref_pre_channels(
                  signal_ref, signal_pre, ch_dim=2, force_1ch=True
              )
              # for the time domain criterions
              with torch.no_grad() if zero_weight else contextlib.ExitStack():
                  l, s, o = loss_wrapper(sref, spre, {**others, **o})
          elif isinstance(criterion, FrequencyDomainLoss):
              sref, spre = self._align_ref_pre_channels(
                  signal_ref, signal_pre, ch_dim=2, force_1ch=False
              )
              # for the time-frequency domain criterions
              if criterion.compute_on_mask:
                  # compute loss on masks
                  if getattr(criterion, "is_noise_loss", False):
                      tf_ref, tf_pre = self._get_noise_masks(
                          criterion,
                          feature_mix,
                          speech_ref,
                          signal_ref,
                          signal_pre,
                          speech_lengths,
                          others,
                          fs=fs,
                      )
                  elif getattr(criterion, "is_dereverb_loss", False):
                      tf_ref, tf_pre = self._get_dereverb_masks(
                          criterion,
                          feature_mix,
                          noise_ref,
                          signal_ref,
                          signal_pre,
                          speech_lengths,
                          others,
                          fs=fs,
                      )
                  else:
                      tf_ref, tf_pre = self._get_speech_masks(
                          criterion,
                          feature_mix,
                          noise_ref,
                          signal_ref,
                          signal_pre,
                          speech_lengths,
                          others,
                          fs=fs,
                      )
              else:
                  # compute on spectrum
                  tf_ref = [
                      self.encoder(sr, speech_lengths, fs=fs)[0]
                      for sr in sref
                  ]
                  # for models like SVoice that output multiple lists of
                  # separated signals
                  pre_is_multi_list = isinstance(spre[0], (list, tuple))
                  if pre_is_multi_list:
                      tf_pre = [
                          [
                              self.encoder(sp, speech_lengths, fs=fs)[0]
                              for sp in ps
                          ]
                          for ps in spre
                      ]
                  else:
                      tf_pre = [
                          self.encoder(sp, speech_lengths, fs=fs)[0]
                          for sp in spre
                      ]
  
              with torch.no_grad() if zero_weight else contextlib.ExitStack():
                  l, s, o = loss_wrapper(
                      tf_ref, tf_pre, {**others, **o}
                  )
          else:
              raise NotImplementedError(
                  "Unsupported loss type: %s" % str(criterion)
              )
  
          loss += l * loss_wrapper.weight
  
          # rename the loss keys with a category prefix
          if (
              self.categories
              and category is not None
              and category[0].item() not in self.categories
          ):
              raise ValueError(
                  f"Category '{category}' is not listed in self.categories"
              )
          if category is not None:
              for idx, c in self.categories.items():
                  if idx == category[0].item():
                      s[criterion.name + "_" + c] = s.pop(
                          criterion.name
                      )
                  else:
                      s[criterion.name + "_" + c] = torch.full_like(
                          loss, np.nan
                      )
          else:
              idx = 0
          stats.update(s)
          loss *= (
              self.category_weights[idx]
              if self.category_weights
              else 1.0
          )
  
          if perm is None and "perm" in o:
              perm = o["perm"]
  
      if self.training and not loss.requires_grad:
          raise AttributeError(
              "Loss must be a tensor with gradient in the training mode. "
              "Please check the following:\n"
              "1. At least one criterion must satisfy: only_for_test=False"
              "2. At least one criterion must always be computed in the training mode"
              " regardless of is_noise_loss=True or is_dereverb_loss=True"
          )
      stats["loss"] = loss.detach()
  
      # force_gatherable: to-device and to-tensor if scalar for DataParallel
      batch_size = speech_ref[0].shape[0]
      loss, stats, weight = force_gatherable(
          (loss, stats, batch_size), loss.device
      )
      return loss, stats, weight, perm
  
imports: import contextlib
  from typing import Dict, List, Optional, OrderedDict, Tuple
  import numpy
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.diar.layers.abs_mask import AbsMask
  from espnet2.enh.decoder.abs_decoder import AbsDecoder
  from espnet2.enh.encoder.abs_encoder import AbsEncoder
  from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainLoss
  from espnet2.enh.loss.criterions.time_domain import TimeDomainLoss
  from espnet2.enh.loss.wrappers.abs_wrapper import AbsLossWrapper
  from espnet2.enh.separator.abs_separator import AbsSeparator
  from espnet2.enh.separator.dan_separator import DANSeparator
  from espnet2.enh.separator.uses_separator import USESSeparator
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
label_desc: Use image node_id 4 for calling the ESPnetEnhancementModel obj's underlying member method code with example usage: obj.forward_loss(speech_pre, speech_lengths, feature_mix, feature_pre, others, speech_ref, noise_ref, dereverb_speech_ref, category, num_spk, fs) and returns: loss,  stats,  weight,  perm